EU AI Act covers 66.7% of NIST AI Risk Management Framework (AI RMF 1.0)
48 of the 72 controls in NIST AI Risk Management Framework (AI RMF 1.0) are already satisfied by evidence you collected for EU AI Act. 24 are genuine gaps. Every claim below was judged against both control sets and then argued against; the ones that did not survive are published further down with the reason each failed.
What this leaves you to do
NIST AI Risk Management Framework (AI RMF 1.0) has 72 controls. Holding EU AI Act already evidences 48 of them, so the work in front of you is 24 controls, not 72, which is 33% of the standard rather than all of it.
That is the whole claim. We do not know your hourly rate, how long a control takes you, or how many people you have, so there is no figure here in dollars or weeks. Every number in that sentence comes from the two counts above it and can be re-derived from the free tools without taking our word for any of it.
This number is directional. It says how much of NIST AI Risk Management Framework (AI RMF 1.0) your EU AI Act evidence satisfies. The reverse pair is a different number, often very different, because a security standard has enormous depth for access control and almost none for lawful basis or data subject rights.
72 candidate mappings were examined and 10 were removed. Signed off 2026-08-20, review level machine verified. Mappings were judged by Claude Code rather than read line by line by a practitioner. Every claim shows its reasoning so you can check it. Ask and a practitioner will review this pair.
Where the gaps are
Coverage is never evenly spread. A source standard usually satisfies one part of a target almost completely and barely touches another, and which part is which is the thing worth knowing before you plan the work.
Theme level, not control level, deliberately. The per-control list of what is evidenced and what is a gap is the report itself, so publishing it here would be publishing the thing being sold.
Claims that held
A sample. Each one names the control whose evidence does the work, the control it satisfies, and why.
The recorded high-risk determination per system is documented understanding of which legal regime binds it.
A written QMS carrying compliance strategy, design control, data management and accountability integrates these characteristics into procedure.
Classification determines which obligation set applies, which is the same tiering-drives-effort mechanism.
A documented, maintained, continuously iterative risk management system is this process and its outcomes.
Registering each system in the EU database before market placement is a maintained AI system inventory.
The QMS accountability framework sets out management and staff responsibilities across the lifecycle.
AI literacy calibrated to knowledge, context and affected persons, for staff and those acting on their behalf.
Article 14 specifies what the overseeing person must be enabled to do, which differentiates the human-AI roles.
Claims that did not hold
10 proposed mappings for this pair were rejected. They are kept in the graph rather than deleted, so what was thrown out is as inspectable as what survived. A crosswalk that never rejects anything is not being judged.
Judged against a node bundling seven distinct provider obligations. The recorded intent says only provider obligations against roles and responsibilities, which sits equally on Art.16, the list of provider duties, and on Art.17(1)(m), the accountability framework inside the quality management system. Two candidate articles is not an unambiguous re-home.
Claimed at refuted confidence before it was rejected.
Refuted in review.
Refuted in review.
Refuted in review.
Refuted in review.
Claimed at unrated confidence before it was rejected.
Refuted in review.
Refuted in review.
Refuted in review.
The full report
Everything above is a sample. The report is every evidenced control and every gap, with the reasoning and the source document behind each one, in a form you can hand to an assessor. $299, emailed immediately.
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